Advancing Toward Robust and Scalable Fingerprint Orientation Estimation: From Gradients to Deep Learning

TL;DR

This paper reviews the evolution of fingerprint orientation estimation methods, emphasizing the need for hybrid approaches that combine traditional gradient techniques with deep learning to improve robustness and scalability in biometric systems.

Contribution

It highlights the limitations of current methods and advocates for hybrid algorithms that integrate gradient-based and deep learning techniques for better performance.

Findings

Current algorithms struggle with degraded images and noise.

Hybrid methods show promise for improved robustness.

Future research should focus on efficient, scalable algorithms.

Abstract

The study identifies a clear evolution from traditional methods to more advanced machine learning approaches. Current algorithms face persistent challenges, including degraded image quality, damaged ridge structures, and background noise, which impact performance. To overcome these limitations, future research must focus on developing efficient algorithms with lower computational complexity while maintaining robust performance across varied conditions. Hybrid methods that combine the simplicity and efficiency of gradient-based techniques with the adaptability and robustness of machine learning are particularly promising for advancing fingerprint recognition systems. Fingerprint orientation estimation plays a crucial role in improving the reliability and accuracy of biometric systems. This study highlights the limitations of current approaches and underscores the importance of designing next-generation algorithms that can operate efficiently across diverse application domains. By addressing these challenges, future developments could enhance the scalability, reliability, and applicability of biometric systems, paving the way for broader use in security and identification technologies.